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J. Hendrik Kappes, Speth, M., Reinelt, G., and Schnörr, C., “Higher-order Segmentation via Multicuts”, ArXiv e-prints. 2013.

J. H. Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Kim, S., Kausler, B. X., Kröger, T., Lellmann, J., Komodakis, N., Savchynskyy, B., and Rother, C., “A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems”, Int.~J.~Comp.~Vision, 2015.

Technical Report (5.12 MB)

J. H. Kappes, Petra, S., Schnörr, C., and Zisler, M., “TomoGC: Binary Tomography by Constrained Graph Cuts”, in Proc.~GCPR, 2015.

Technical Report (2.46 MB)

J. H. Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., “Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts”, in Proc.~SSVM, 2015.

Technical Report (1.1 MB)

J. H. Kappes, Schmidt, S., and Schnörr, C., “MRF Inference by k-Fan Decomposition and Tight Lagrangian Relaxation”, in European Conference on Computer Vision (ECCV), 2010, vol. 6313, p. 735--747.

Technical Report (1.49 MB)

J. H. Kappes and Schnörr, C., “MAP-Inference for Highly-Connected Graphs with DC-Programming”, in Pattern Recognition -- 30th DAGM Symposium, 2008, vol. 5096, p. 1--10.

Technical Report (1.91 MB)

J. H. Kappes, Speth, M., Andres, B., Reinelt, G., and Schnörr, C., “Globally Optimal Image Partitioning by Multicuts”, in EMMCVPR, 2011.

Technical Report (7.47 MB)

J. H. Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Kim, S., Kausler, B. X., Kröger, T., Lellmann, J., Komodakis, N., Savchynskyy, B., and Rother, C., “A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems”, CoRR, vol. abs/1404.0533, 2014.

Technical Report (3.32 MB)

J. H. Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Kim, S., Kausler, B. X., Lellmann, J., Komodakis, N., and Rother, C., “A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problem”, in CVPR, 2013.

Technical Report (1.35 MB)

J. H. Kappes, Savchynskyy, B., and Schnörr, C., “A Bundle Approach To Efficient MAP-Inference by Lagrangian Relaxation”, in CVPR, 2012.

Technical Report (430.63 KB)

J. H. Kappes, Speth, M., Reinelt, G., and Schnörr, C., “Towards Efficient and Exact MAP-Inference for Large Scale Discrete Computer Vision Problems via Combinatorial Optimization”, in CVPR, 2013.

Technical Report (623.84 KB)

J. H. Kappes, Speth, M., Reinelt, G., and Schnörr, C., “Higher-order Segmentation via Multicuts”, ArXiv e-prints. 2013.

Technical Report (1.07 MB)

J. H. Kappes, Beier, T., and Schnörr, C., “MAP-Inference on Large Scale Higher-Order Discrete Graphical Models by Fusion Moves”, in Computer Vision - {ECCV} 2014 Workshops - Zurich, Switzerland, September 6-7 and 12, 2014, Proceedings, Part {II}, 2014.

Technical Report (557.49 KB)

J. H. Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Kim, S., Kausler, B. X., Kröger, T., Lellmann, J., Komodakis, N., Savchynskyy, B., and Rother, C., “A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems”, International Journal of Computer Vision, pp. 1-30, 2015.

Technical Report (1.5 MB)

J. H. Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Sungwoong, K., Kausler, B. X., Lellmann, J., Komodakis, N., and Rother, C., “A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems”, in CVPR 2013. Proceedings, 2013.

Technical Report (1.35 MB)

J. H. Kappes, Speth, M., Andres, B., Reinelt, G., and Schnörr, C., “Globally Optimal Image Partitioning by Multicuts”, in EMMCVPR, 2011, pp. 31-44.

Technical Report (7.3 MB)

J. H. Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Kim, S., Kausler, B. X., Kröger, T., Lellmann, J., Komodakis, N., Savchynskyy, B., and Rother, C., “A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems”, CoRR, 2014.

J. H. Kappes, Savchynskyy, B., and Schnörr, C., “A Bundle Approach To Efficient MAP-Inference by Lagrangian Relaxation”, in CVPR. Proceedings, 2012, pp. 1688-1695.

J. H. Kappes, Schmidt, S., and Schnörr, C., “MRF Inference by k-Fan Decomposition and Tight Lagrangian Relaxation”, in European Conference on Computer Vision (ECCV), 2010, vol. 6313, p. 735--747.

R. Karim, Bergtholdt, M., Kappes, J. H., and Schnörr, C., “Greedy-Based Design of Sparse Two-Stage SVMs for Fast Classification”, in Pattern Recognition – 29th DAGM Symposium, 2007, vol. 4713, pp. 395-404.

R. Karim, Bergtholdt, M., Kappes, J. H., and Schnörr, C., “Greedy-Based Design of Sparse Two-Stage SVMs for Fast Classification”, in Pattern Recognition -- 29th DAGM Symposium, 2007, vol. 4713, pp. 395-404.

Technical Report (491.56 KB)

S. Kassemeyer, “Quantification of Tumour Angiogenesis Using Pattern Recognition”, University of Heidelberg, 2009.

F. O. Kaster, “Image Analysis for the Life Sciences - Computer-assisted Tumor Diagnostics and Digital Embryomics”. University of Heidelberg, 2011.

F. O. Kaster, Kelm, B. Michael, Zechmann, C. M., Weber, M. - A., Hamprecht, F. A., and Nix, O., “Classification of Spectroscopic Images in the DIROlab Environment”, in World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany, 2009, vol. 25/V, p. 252--255.

Technical Report (145.73 KB)

F. O. Kaster, Weber, M. - A., and Hamprecht, F. A., “Comparative Validation of Graphical Models for Learning Tumor Segmentations from Noisy Manual Annotations”, in LNCS, 2011, vol. LNCS 6533, pp. 74-85.

Technical Report (544.56 KB)

F. O. Kaster, Kassemeyer, S., Merkel, B., Nix, O., and Hamprecht, F. A., “An object-oriented library for systematic training and comparison of classifiers for computer-assisted tumor diagnosis from MRSI measurements”, in Bildverarbeitung für die Medizin 2010 -- Algorithmen, Systeme, Anwendungen, 2010, pp. 97-101.

Technical Report (1.12 MB)

F. O. Kaster, Merkel, B., Nix, O., and Hamprecht, F. A., “An object-oriented library for systematic training and comparison of classifiers for computer-assisted tumor diagnosis from MRSI measurements”, Computer Science - Research and Development, vol. 26, pp. 65-85, 2011.

Technical Report (808.16 KB)

J. P. Kauppi, Kandemir, M., Saarinen, V. M., Hirvenkari, L., Parkkonen, L., Klami, A., Hari, R., and Kaski, S., “Towards brain-activity-controlled information retrieval: Decoding image relevance from MEG signals”, NeuroImage, vol. 112, pp. 288-298, 2015.

Technical Report (2.39 MB)

B. X. Kausler, “Tracking-by-Assignment as a Probabilistic Graphical Model with Applications in Developmental Biology”. University of Heidelberg, 2013.

B. X. Kausler, “Modeling of Spectral Peaks for Mass-Spectrometry-based Proteomics”, Universities of Karlsruhe and Heidelberg, 2010.

B. X. Kausler, Schiegg, M., Andres, B., Lindner, M., Köthe, U., Leitte, H., Wittbrodt, J., Hufnagel, L., and Hamprecht, F. A., “A Discrete Chain Graph Model for 3d+t Cell Tracking with High Misdetection Robustness”, in ECCV 2012. Proceedings, 2012, vol. 7574, pp. 144-157.

Technical Report (809.07 KB)

D. Kawetzki, “Semantic Segmentation of Urban Scenes Using Deep Learning”, Heidelberg University, 2018.

B. Michael Kelm, “Evaluation of Vector-Valued Clinical Image Data Using Probabilistic Graphical Models: Quantification and Pattern Recognition”. University of Heidelberg, 2007.

Technical Report (4.89 MB)

B. Michael Kelm, Kaster, F. O., Henning, A., Weber, M. - A., Bachert, P., Bösinger, P., Hamprecht, F. A., and Menze, B. H., “Using Spatial Prior Knowledge in the Spectral Fitting of Magnetic Resonance Spectroscopic Images”, NMR in Biomedicine, vol. 25(1), pp. 1-13, 2011.

Technical Report (1.94 MB)

B. Michael Kelm, Müller, N., Menze, B. H., and Hamprecht, F. A., “Bayesian Estimation of Smooth Parameter Maps for Dynamic Contrast-Enhanced MR Images with Block-ICM”, in Proc Computer Vision and Pattern Recognition Workshop (Mathematical Methods in Biomedical Image Analysis), 2006, pp. 96-103.

Technical Report (232.69 KB)

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